Papers with text-to-text methods

1 papers
JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning (2025.findings-acl)

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Challenge: Existing text-to-text methods struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures.
Approach: They propose a structure-to-structure approach that uses JSON structures to represent tasks.
Outcome: The proposed method outperforms TextTuning in terms of performance, robustness, and controllability across different scenarios.

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